I have received a number of Twitter questions asking how to interpret the Best Case and Worst Case Scenarios in my projections. If you think of a normal bell curve, while most of the mass is in the middle, the tails can stretch out for some distance. I don’t think there is much value in trying to present the full tail for each team when projecting the season. If I reported the true outliers for every team, every team’s range would be ridiculously large. I tried to settle on cut-offs that communicate the relative riskiness of teams.
The real question is how often teams fall within my Best Case/Worst Case range. I have an idea based on past seasons, but since I used those seasons to fit the model, I’m not quite willing to make a definitive statement on that question yet. For now, let me present a couple of outliers from last year.
- What would my new simulation model have projected for Kentucky and Michigan for 2012-2013?
Median Simulation : 16th
Best Case: 4th
Worst Case: 43rd
While most of us fell in love with the upside for Kentucky’s starting lineup last year, what we were not accounting for was the fact that Kentucky had very little depth. If Kentucky’s starters were injured or struggled, the downside simulations were quite weak. In fact, based on the number of available at-large bids, Kentucky’s worst case scenario was that of a borderline NCAA/NIT team last year.
And as we saw, the worst case scenario came to fruition. According to Sagarin’s margin-of-victory-based “Predictor”, Kentucky finished 38th last season. According to Ken Pomeroy’s old MOV formula, Kentucky finished 48th. And according to Ken Pomeroy’s new capped MOV formula, Kentucky finished 67th last season.
Median Simulation: 23rd
Best Case: 6th
Worst Case: 57th
As with Kentucky, Michigan had a relatively large range for a Top 25 team. And the reason for those large ranges is because both teams were relying a ton on freshmen last year. The performance of freshmen is extremely unpredictable. In the end, Michigan finished above my best case scenario at fourth or fifth depending on your preferred MOV system.
While these finishes were just outside my projected range, I am comfortable with both of these. That is because I believe in both cases those were true outlier seasons, far out in the tail.
Despite having what the experts labeled as the 8th-12th best recruiting class in the country, Michigan’s freshmen class was by far the most productive in the country last year. To have freshman Nik Stauskas come in and make 80 threes, to have Mitch McGary play like a superstar in the NCAA tournament, and to have players like Spike Albrecht come out of nowhere and play mistake-free basketball was incredible.
Meanwhile, for Kentucky, just about everything that could go wrong, did go wrong. From the injury to Nerlens Noel, to the disappointing play of a highly touted transfer PG, to John Calipari’s rare failure to get the Wildcats to buy-in on defense, everything broke the wrong way.
If these are the type of seasons that fall just outside my projected range, I feel fairly confident in the accuracy of my system.
- What does this mean for 2013-2014?
While I am not guaranteeing that Kentucky will finish in the Top 13 this year, my model is essentially saying that this is extremely likely.
Kentucky simply has too much depth for things to completely fall apart this year. As I noted last week, Julius Randle could be a massive underachiever and Will-Cauley Stein could get hurt, and Kentucky would not miss a beat. The only possible weakness on the Wildcats is the lack of depth at the guard positions.
But with a downside of 13th this year instead of 43rd last year, Kentucky fans can be confident that even if things go wrong, the team will still be relevant in March.
- Didn’t I have Michigan rated lower than 23rd in last year’s preseason projections?
Yes, absolutely. Michigan is a huge reason that I added the simulation to the model. What I wanted to be able to do was more effectively emphasize the importance of star players. It is much easier for the winner of a competition to be a role player. And because Michigan had Trey Burke (and to a lesser extent Tim Hardaway), they already had their stars last year. They only had to find role players to fill in around them. I agree that my old model was too pessimistic, and Michigan is a large reason I added a simulation to my model this year.
This also explains why Michigan’s upside remains extremely strong this year. Again, Michigan is going to be relying a lot on unproven players. But with Mitch McGary and Glenn Robinson leading the way, if this year’s guards click, the upside for Michigan remains that of a Top 5 team. (The Wolverines also needs McGary to get over his lingering back issues.)
But the real importance of the simulation is the earlier note about depth. This year Maryland, Alabama, NC State, Temple and Vanderbilt have very short benches. Those teams might have competitive rotations, but the lack of scholarship players is a risk. Do not be surprised if injuries derail the season for at least one of these teams.